What is Customer Analytics and Why is it important?

Companies are investing heavily in understanding consumer behaviour since today, to overcome competition. As a result, customer analytics has become a process that many companies are adopting. Here’s more about the importance of customer analytics.

What is Customer Analytics?

Customer analytics is the process where data collected from different sources is analysed to determine, attract, and maintain customers. It helps businesses draw useful insights, create more useful products, deliver better services, and develop more profitable business decisions.

A well-planned customer analytics approach further helps in foreseeing customer behaviour in a better way. This analysis is also, applied in marketing and sales to develop customer relationships and build a brand position in the market.

How Customer Analytics Works

Customer analytics involves collecting and analyzing data from various customer touchpoints to gain insights into customer behavior, preferences, and needs. By leveraging techniques such as data mining, predictive modeling, and segmentation, businesses can uncover valuable patterns and trends that help them understand their customers better. This understanding allows companies to tailor their marketing efforts, improve customer experiences, and optimize product offerings to meet the ever-changing demands of their target audience. Ultimately, customer analytics empowers businesses to make data-driven decisions that drive growth, enhance customer satisfaction, and maximize profitability.

What is the importance of Customer Analytics?

In the current scenario where customers are more empowered and linked with companies than before; customer analytics has evolved as a critical parameter. The more a company knows about its customers’ habits and preferences; the more successful they become at delivering suitable recommendations and attracting new customers.
Customer analytics is of growing importance as:

• Companies can understand their shortcomings as well as opportunities using the customers’ behavioural records.
• When consumers have access to all information like where to purchase from, what to purchase, how to make payment, etc.; customers’ preferences help companies interact with them more efficiently.
• Precise knowledge of customers’ buying patterns and preferences helps design future products and services that would bring profit to the company.
• 360-degree data analytics enables companies to gain a holistic viewpoint of customers, which helps design marketing and sales strategies to bring new customers.
• Customer satisfaction is important to retain customers for the website or company. Predictive customer analysis helps build innovative techniques to improve customer satisfaction.
• Well-designed customer analysis helps increase customer loyalty, further increasing the ROI.
• Well analysed customer analytics helps target suitable customers at the correct time, thus reducing the campaign costs.

What are the Main Categories of Customer Analytics?

Customer analytics is a great tool for companies to understand customers’ behaviour. To tailor different services and meet market challenges according to the analysis, it’s important to understand the main categories of customer analytics. The 4 main categories are as follows:

Descriptive Analytics

Descriptive analytics provides an understanding of past customer behaviour by describing:

• Customer details
• Customer browsing history
• Customer purchase and return information
• Transactional pattern, etc.

Diagnostic analytics

Diagnostic analytics helps predict root problems or why customers behave in a certain way. It primarily helps understands the logic behind customer behaviour.
Diagnostic analysis covers:

• Data discovery
• Drilling information
• Developing correlations between the pieces of information

The diagnostic analysis helps answer specific types of questions and discover new possibilities, reducing several challenges.

Predictive analytics

Predictive analytics takes into consideration past as well as current data to draw patterns and define future possibilities. It is also helpful in building models for revenue generation and maximising the ROI.

Prescriptive analytics

Prescriptive analytics provides suitable action for a particular scenario. It also helps build a personalised customer experience and chart the next set of actions for individual users. It enables companies to understand:

• What buyers are looking for?
• Whom to sell a particular product?
• What type of products do consumers prefer to buy?
• How to increase the number of customers?

Top Customer Analytics Tools

Customer analytics tools are applications designed to offer insights into customer behaviour. This helps customise marketing campaigns and build strategies to meet customers’ specific demands.
The top-rated customer analytics tools used for data analysis, data sourcing and visualisation are Adobe Analytics, IBM Watson Customer Experience Analytics, Google Analytics 360, SAP Hybris Marketing Cloud, SAS Customer Intelligence 360, etc.

To sum it all up

Customer analytics plays a huge role in the growth and success of a business, as it helps precisely understand consumers’ likings, preferences, and dislikes. Thus, customer analytics can be helpful if a company is looking forward to improving its marketing campaign, leveraging brand loyalty or increasing return on investment on customer acquisition costs.
A well-planned customer analysis helps predict customers’ reactions to the product, services, AD campaigns, and brand adaptation. In addition, it helps understand customer experiences and satisfaction.

You can learn more about customer analytics, data analytics and related topics through data analytics courses. They will help you gain an in-depth understanding of data structures, and their analysis to explore meaningful insights.

Emeritus India offers a range of courses on data analytics, data science and various related technologies in partnership with institutes like IIM, IIT and ISB, to help you build managerial expertise in the tools and techniques used in data analytics.